Better known now for his seminal influence on bible studies, Galileo was the first to verify experimentally the law of inertia (1): steady motion continues indefinitely unless disturbed by external forces. For physicists, it means rest cannot be distinguished from steady motion. For the rest of us and after a month of steady media coverage (see 09/12 and 09/19 fillips), it means another article on the Hewlett Packard board scandal (2) carries as much information as no news at all.

Meanwhile the 109th US Congress is happy to let the data rights of ordinary citizens rest in peace (see 10/03 fillip). On the tombstone, we find listed the numerous Bills who once were part of the family: S.751 with the eternal regrets of Dianne Feinstein, S.768 with the same from Charles Schumer not to mention their five sibblings (3). Congress of course is all bogged down in the supervision of minor details (4).

If one looks for less depressing and more interesting news, I recommend a recent article filed by Katie Hafner (*) about an initiative by Netflix, the movie rental company.

Netflix' business model rests on its customers ordering by mail as many titles as they want, as long as they return the last one in order to get the next one. Since few people watch more than one movie at a time, Netflix gets to satisfy everyone as long as it insures a fast enough turn around time. As with any physical lending library, this requires Netflix to stock each DVD in proportion to its popularity. Assume the warehouse carries 40 copies of "An Inconvenient Truth" (5). If it receives 100 orders, customers per force must wait. If it receives 10 orders, Netflix finances dead assets. In both cases an inconvenient truth indeed.

Netflix has therefore invested wisely and built a recommendation mechanism (see 08/22 fillip), the type of which we have not yet encountered (see 09/12 and 09-26 fillips). For the consumer, a Netflix recommendation is just an easy way to pick among a dizzying array of choices. For Netflix, assume for a minute that its recommendations are faithfully followed. The recommendation mechanism now becomes an excellent predictive system to forecast demand. With it, Netflix does not merely record past popularity, it can shape its future. At the scale of Netflix operations, even a small improvement in forecast accuracy translates into increased customer satisfaction and decreased warehousing costs. Even better, when the day comes for Netflix to go digital or die, a good recommendation system may be the key to its survival.

There is nothing wrong with the fact that a recommendation mechanism benefits its owner. Google's business itself is based on a double recommendation mechanism, one to rank the free results coming from its seach engine and the other to list the companion paid for ads (09/12 fillip). A profitable mechanism makes it more likely that the owner will devote the required resources to guarantee optimal performance. I rest my case with the challenge issued by Netflix to anyone who dares to respond: $1 Million to the first person who can improve the accuracy of its current system by at least 10 percent. Brilliant (**).

As any recommendation mechanism though, Netflix' current and future solutions must be examined in the light of our three criteria.

degree of centralization

responsibility

protest process

As a recommendation mechanism, Netflix has one advantage over Google. Netflix may dream of becoming the next Amazon. For now however its relatively narrow focus on consumer movie preferences is a form of decentralization. From a privacy perspective, it is easier to remove personal identification patterns from individual tastes when they are restricted to one domain than when they cover all possible topics under the sun (see 08-29 fillip). Netflix management is actually quite aware of the issue, a good sign.

Moreover the principle of Netflix' mechanism is decentralized in the extreme. Recommendations are based on an individual's past preferences, informed by the analysis of the behavior of all other customers. If the majority of those who enjoyed "Supersize me" (6) also enjoyed "An Inconvenient Truth" and John Doe has seen the former but not the latter, it stands to reason to recommend him the latter. That a good recommendation mechanism is actually a bit more complex and justifies the $1 Million reward posted by Netflix is besides the point. The fact is movie watchers are gregarious, much like sheep, and analysis based on flock behavior works.

Unfortunately we need to throw a warning. Movie marketing talent has as much an incentive to bias Netflix' recommendations as Search Engine Optimizers to influence their clients' Google ranking. The dark side has compelled Google to obscure its ranking algorithm behind a layer of obfuscation, thereby recentralizing the recommendation mechanism to the extreme. While Netflix promises to reveal the innards of the prize winning entry, the fine print of its rules hints that its current mechanism has already many undisclosed features. Were I Reed Hastings, Netflix' CEO, I would issue a second challenge with an equal purse. Publicly describe a "collaborative filtering" scheme which can be proven to cost third parties as much to bias than they can expect to gain from such a bias. I bet Professors Laura Frieder and Jonathan Zittrain would volunteer to sit on the jury (***).

It follows that, as long as Netflix does not explicitly address the dark side, it takes the ultimate responsibility for the recommendations its mechanism makes. But just like Google and ID verification services, it fails to recognize the potential for deliberate errors (7) and provides no redress process.

Yet more than five hundred years ago, Panurge (8) had already shown how to exploit gregarious behavior for mischief. A sheep merchant he meets on board a ship crosses him greatly. To revenge himself, Panurge buys the best beast for a hefty sum and throws his newly acquired sheep overboard. Following their leader, the rest of the flock jumps into the sea. In an attempt to check his losses the merchant vainly clings to one of his flock and drowns likewise.

Philippe Coueignoux

(*)......And if you like the Movie, a Netflix Contest May Reward You Hansomely, by Katie Hafner (New York Times) - October 2, 2006